MACHINE LEARNING

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Solicitudes publicadas en los últimos 30 días / Applications published in the last 30 days



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AIML-BASED CONTINUOUS DELIVERY FOR NETWORKS

Publication No.: US2023022050A1 26/01/2023

Applicant:

EMC IP HOLDING COMPANY LLC [US]

Absstract of: US2023022050A1

One example method includes deploying an application in a distributed computing environment. Telemetry data is collected that corresponds with the deployment of an application. The telemetry data is received by a machine learning model that was trained with test telemetry data to determine whether the deploying is successful or failed. A successful inference results in continued deployment and a fail inference results in a rollback of the application.

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DIGITAL THERAPEUTIC PLATFORM

Publication No.: US2023022047A1 26/01/2023

Applicant:

EYETHENA CORP [US]

WO_2022173872_A1

Absstract of: US2023022047A1

Systems and methods are provided for monitoring health. An exemplary method includes: collecting a first data regarding a patient during an in-office visit; providing a remote monitoring service for remotely monitoring the patient's health; remotely collecting, using the remote monitoring service, a second data of the patient; providing a probabilistic network for assigning metric-based information to the plurality of data using a plurality of conditional probabilities; processing, using the probabilistic network, the first data and the second data using the probabilistic network; generating, using the processed plurality of data, one or more machine learning models for producing a knowledge base trained to recognize pattern types in the data; generating, using the knowledge base, one or more artificial intelligent features for recommending treatment options based on the data regarding the patient; and providing, using the one or more artificial intelligent features, one or more treatment recommendations for improving the patient's health.

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Hybrid Machine Learning and Knowledge Graph Approach for Estimating and Mitigating the Spread of Malicious Software

Publication No.: US2023026135A1 26/01/2023

Applicant:

BANK OF AMERICA CORP [US]

Absstract of: US2023026135A1

Aspects of the disclosure relate to predicting the spread of malicious software. The computing platform may identify malicious software at a computing device and may input characteristics of the malicious software into a machine learning model to produce time horizons for the malicious software. The computing platform may identify, using a knowledge graph and based on the time horizons, subsets of computing devices, each corresponding to a particular time horizon. The computing platform may perform, at a time within a first time horizon, a first security action for a first subset of computing devices within the first time horizon and a second security action for a second subset of computing devices located within a second time horizon, where the first time horizon and the second time horizon indicate that the first subset will be affected by the malicious software prior to the second subset.

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Method of Transfer Learning for a Specific Production Process of an Industrial Plant

Publication No.: US2023023896A1 26/01/2023

Applicant:

ABB SCHWEIZ AG [CH]

CN_115362454_PA

Absstract of: US2023023896A1

A method of transfer learning for a specific production process of an industrial plant includes providing data templates defining expected data for a production process, and providing plant data, wherein the data templates define groupings for the expected data according to their relation in the industrial plant; determining a process instance and defining a mapping with the plant data; determining historic process data; determining training data using the determined process instance and the determined historic process data, wherein the training data comprises a structured data matrix, wherein columns of the data matrix represent the sensor data that are grouped in accordance with the data template and wherein rows of the data matrix represent timestamps of obtaining the sensor data; providing a pre-trained machine learning model using the determined process instance; and training a new machine learning model using the provided pre-trained model and the determined training data.

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System and Method for Throughput Prediction for Cellular Networks

Publication No.: US2023024501A1 26/01/2023

Applicant:

AT&T INTELLECTUAL PROPERTY I L P [US]
UNIV COLLEGE CORK NATIONAL UNIV OF IRELAND [IE]

US_2020252147_A1

Absstract of: US2023024501A1

Aspects of the subject disclosure may include, for example, a method in which a processing system identifies a plurality of performance indicators comprising device performance indicators for a plurality of communication devices on a cellular network and network performance indicators for the cellular network. The method also includes obtaining historical data regarding the plurality of performance indicators for each of a series of time points during a past time period; the historical data for each of the plurality of performance indicators form an array of values for that performance indicator. The method further includes generating from each array a set of inputs to an algorithm for predicting a throughput of the cellular network during a future time period; the set of inputs comprises quantiles of the array, and the algorithm comprises a machine learning algorithm. Other embodiments are disclosed.

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Method of Hierarchical Machine Learning for an Industrial Plant Machine Learning System

Publication No.: US2023029400A1 26/01/2023

Applicant:

ABB SCHWEIZ AG [CH]

CN_115362454_PA

Absstract of: US2023029400A1

A method of hierarchical machine learning includes receiving a topology model having information on hierarchical relations between components of the industrial plant, determining a representation hierarchy comprising a plurality of levels, wherein each representation on a higher level represents a group of representations on a lower level, wherein the representations comprise a machine learning model, and training an output machine learning model using the determined hierarchical representations.

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SYSTEM AND METHOD FOR NON-DESTRUCTIVE RAPID FOOD PROFILING USING ARTIFICIAL INTELLIGENCE

Publication No.: US2023029413A1 26/01/2023

Applicant:

PROFILEPRINT PTE LTD [SG]

CN_114761788_PA

Absstract of: US2023029413A1

A system and method for non-destructive food rapid profiling in terms of taste, variant classification, adulteration, etc., using artificial intelligence. The system includes: a receptacle configured to move a non-homogenized sample in a path to intersect a volumetric sampling space; a sensor configured to sense reflectance from at least a part of the sample in the volumetric sampling space, the sensor being configured to output a component of the reflectance as captured data, the captured data being characterised by an overtone spectrum over a range of wavelengths; and a computing device configured to apply at least one first machine learning model to the captured data to: predict at least one facet corresponding to predictively determined selected wavelengths; and provide a signature data using the at least one facet.

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MACHINE LEARNING POWERED ANOMALY DETECTION FOR MAINTENANCE WORK ORDERS

Publication No.: US2023027594A1 26/01/2023

Applicant:

ESMALIFALAK MOHAMMAD [CA]
IYENGAR AKSHAY [CA]
MIRHOSEININEJAD SEYEDMORTEZA [CA]
DOULAS PETER [CA]
EMERY FRANCIS [CA]
MATHEWSON TAYLOR [CA]
HOGAN WILLIAM [CA]
YU MIN HUA [CA]
FIIX INC [CA]

EP_4123528_PA

Absstract of: US2023027594A1

An industrial work order analysis system applies statistical and machine learning analytics to both open and closed work orders to identify problems and abnormalities that could impact manufacturing and maintenance operations. The analysis system applies algorithms to learn normal maintenance behaviors or characteristics for different types of maintenance tasks and to flag abnormal maintenance behaviors that deviate significantly from normal maintenance procedures. Based on this analysis, embodiments of the work order analysis system can identify unnecessarily costly maintenance procedures or practices, as well as predict asset failures and offer enterprise-specific recommendations intended to reduce machine downtime and optimize the maintenance process.

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MACHINE LEARNING FOR CATEGORIZING TEXT

Publication No.: US2023026656A1 26/01/2023

Applicant:

THE BOEING COMPANY [US]

Absstract of: US2023026656A1

A method of categorizing natural language text using a processor configured to execute instructions stored on a memory to perform the steps. The method includes selecting candidate keywords from a list of potential keywords based on the natural language text, the candidate keywords having a probability of success being greater than a threshold value. The method also includes generating, using a classification machine learning model (MLM), a list of candidate categories based on the potential keywords. The method also includes generating, using a similarity comparison MLM, a similarity score based on the candidate categories and a set of pre-determined categories. The method also includes assigning a selected category based on the similarity score to the natural language text.

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Neural-Symbolic Action Transformers for Video Question Answering

Publication No.: US2023027713A1 26/01/2023

Applicant:

INT BUSINESS MACHINES CORPORATION [US]

Absstract of: US2023027713A1

Mechanisms are provided for performing artificial intelligence-based video question answering. A video parser parses an input video data sequence to generate situation data structure(s), each situation data structure comprising data elements corresponding to entities, and first relationships between entities, identified by the video parser as present in images of the input video data sequence. First machine learning computer model(s) operate on the situation data structure(s) to predict second relationship(s) between the situation data structure(s). Second machine learning computer model(s) execute on a received input question to predict an executable program to execute to answer the received question. The program is executed on the situation data structure(s) and predicted second relationship(s). An answer to the question is output based on results of executing the program.

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REALISTIC COUNTERFACTUAL EXPLANATION OF MACHINE LEARNING PREDICTIONS

Publication No.: US2023025731A1 26/01/2023

Applicant:

INT BUSINESS MACHINES CORPORATION [US]

Absstract of: US2023025731A1

A computer-implemented method comprising, automatically: analyzing a machine learning dataset which comprises multiple datapoints, to deduce constraints on features of the datapoints; generating a first set of CSP (Constraint Satisfaction Problem) rules expressing the constraints; based on a machine learning model which was trained on the dataset, generating a second set of CSP rules that define one or more perturbation candidates among the features of one of the datapoints; formulating a CSP based on the first and second sets of CSP rules; solving the formulated CSP using a solver; and using the solution of the CSP as a counterfactual explanation of a prediction made by the machine learning model with respect to the one datapoint.

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SPECTRAL ANALYSIS AND MACHINE LEARNING FOR DETERMINING CLUSTER EFFICIENCY DURING FRACKING OPERATIONS

Publication No.: US2023025091A1 26/01/2023

Applicant:

ORIGIN ROSE LLC [US]

US_2022381934_A1

Absstract of: US2023025091A1

This disclosure presents systems, methods, and apparatus for determining cluster efficiency during hydraulic fracturing, the method comprising: measuring acoustic vibrations in fracking fluid in a fracking wellhead, circulating fluid line, or standpipe of a well; converting the acoustic vibrations into an electrical signal in a time domain; recording the electrical signal to memory; analyzing the electrical signal in the time domain for a window of time and identifying two amplitude peaks corresponding to a fracture initiation; measuring a time between the two amplitude peaks; dividing the time by two to give a result; multiplying the result by a speed of sound in the fracking fluid to give a distance between the fracture initiation and a plug at an end of a current fracking stage of the well; and returning a location of the fracture initiation to an operator based on the distance between the fracture initiation and the plug.

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GENERATING AND ADJUSTING DECISION-MAKING ALGORITHMS USING REINFORCEMENT MACHINE LEARNING

Publication No.: US2023022268A1 26/01/2023

Applicant:

INTUIT INC [US]

Absstract of: US2023022268A1

Certain aspects of the present disclosure provide techniques for updating a policy of an agent, including receiving a first transaction file associated with an entity; predicting, by the agent, an expected reward for each respective string of a plurality of strings associated with the first transaction file based on a policy of the agent, wherein the policy is determined based on a context comprising at least an attribute of the entity; determining a first string based on a highest expected reward; providing, to an environment, the first string; receiving a response to the first string, wherein the response comprises an actual reward; and updating the policy of the agent based on the response to the first string.

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Industrial Plant Machine Learning System

Publication No.: US2023019201A1 19/01/2023

Applicant:

ABB SCHWEIZ AG [CH]

CN_115362454_PA

Absstract of: US2023019201A1

An industrial plant machine learning system includes a machine learning model, providing machine learning data, an industrial plant providing plant data and an abstraction layer, connecting the machine learning model and the industrial plant, wherein the abstraction layer is configured to provide standardized communication between the machine learning model and the industrial plant, using a machine learning markup language.

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SYSTEM FOR AUTOMATED MALICIOUS SOFTWARE DETECTION

Publication No.: US2023019837A1 19/01/2023

Applicant:

SOOS LLC [US]

US_11436330_PA

Absstract of: US2023019837A1

A system for automated malicious software detection includes a computing device, the computing device configured to receive a software component, identify at least an element of software component metadata corresponding to the software component, determine a malicious quantifier as a function of the software component metadata, wherein determining the malicious quantifier further comprises obtaining a source repository, the source repository including at least an element of source metadata, and determining the malicious quantifier as a function of the at least an element of software component metadata and the at least an element of source repository metadata using a malicious machine-learning model, and transmit a notification as a function of the malicious quantifier and a predictive threshold.

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IMPROVING SOFTWARE DEFINED NETWORKING CONTROLLER AVAILABILITY USING MACHINE LEARNING TECHNIQUES

Publication No.: US2023015709A1 19/01/2023

Applicant:

ERICSSON TELEFON AB L M [SE]

WO_2021111455_A1

Absstract of: US2023015709A1

A method of managing a controller of a software defined networking (SDN) network is implemented by a computing device in the SDN network. The method includes receiving status information for the controller, receiving usage information for the operating environment, generating at least one failure prediction for the controller based on the received status information, and outputting prediction information for the at least one failure prediction.

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MACHINE LEARNING WORKFLOW FOR PREDICTING HYDRAULIC FRACTURE INITIATION

Publication No.: US2023012733A1 19/01/2023

Applicant:

SAUDI ARABIAN OIL CO [SA]

WO_2023283544_PA

Absstract of: US2023012733A1

Systems and methods include a computer-implemented method for predicting hydraulic fracture initiation. A fracking operations dataset is prepared using historical field information for fracking wells. A set of hyper-parameters is tuned for use in a machine learning algorithm configured to predict fracture initiation for new fracturing wells. The dataset is divided into training and test datasets. A regression algorithm is applied to train the training dataset and to validate with the test dataset. A target variable of a breakdown pressure for a new hydraulic fracturing treatment is determined. A prediction dataset is updated using at least the target variable. The training dataset is trained using a classifier of the machine learning algorithm. A prediction is made using the prediction dataset whether the new hydraulic fracturing treatment can be initiated or not. The breakdown pressure is incrementally adjusted, and the method is repeated until successful hydraulic fracture initiation is predicted.

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SYSTEMS AND METHODS FOR MACHINE LEARNING BASED COLLISION AVOIDANCE

Publication No.: US2023019376A1 19/01/2023

Applicant:

VERIZON PATENT & LICENSING INC [US]

US_2021312811_A1

Absstract of: US2023019376A1

A device may include a memory storing instructions and processor configured to execute the instructions to receive information relating to a plurality of vehicles in an area. The device may be further configured to use a trained machine learning model to determine a likelihood of collision by one or more of the plurality of vehicles; identify one or more relevant vehicles of the plurality of vehicles that are in danger of collision based on the determined likelihood of collision; and send an alert indicating the danger of collision to at least one of the identified one or more relevant vehicles.

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Distributed Machine-Learning Resource Sharing and Request Routing

Publication No.: US2023020939A1 19/01/2023

Applicant:

AVAGO TECH INT SALES PTE LID [SG]

CN_114885029_PA

Absstract of: US2023020939A1

Various embodiments of the present disclosure improve existing multi-layer and other network technologies by routing and processing client requests that require machine learning based on the machine learning capabilities of each network device and/or other computer resource characteristics of different network devices. This ensures that network latency and throughput, among other computer resource consumption characteristics, will be improved as machine learning processing can occur at the most suitable network device or be distributed among various suitable network devices.

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PREDICTION OF VENOUS THROMBOEMBOLISM UTILIZING MACHINE LEARNING MODELS

Publication No.: US2023019900A1 19/01/2023

Applicant:

HENRY M JACKSON FOUND FOR THE ADVANCEMENT OF MILITARY MEDICINE [US]
US GOV SEC ARMY [US]
US NAVY [US]

WO_2021113510_A1

Absstract of: US2023019900A1

The present disclosure describes methods and systems for predicting if a subject has an increased risk of having or developing venous thromboembolism, including prior to the detection of symptoms thereof and/or prior to onset of any detectable symptoms thereof. The present disclosure also describes a method of generating a model for predicting venous thromboembolism.

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AUTOMATED VALUATION MODEL USING A SIAMESE NETWORK

Publication No.: US2023020771A1 19/01/2023

Applicant:

OPENDOOR LABS INC [US]

US_2022084079_A1

Absstract of: US2023020771A1

Systems and methods are disclosed for automatically determining property value, the systems and methods perform operations comprising: receiving, by a server, subject real-estate property listing information associated with a subject real-estate property; identifying a plurality of comparable real-estate property listings based on attributes of the subject real-estate property listing information; processing the subject real-estate property listing information together with the plurality of comparable real-estate property listings using a trained machine learning technique to predict a value for the subject real-estate property, the trained machine learning technique being trained to jointly establish a relationship between weights assigned to a set of training comparable real-estate property listings and value adjustments of the set of training comparable real-estate property listings and a value of a real-estate property of interest; and performing an action with respect to the subject real-estate property based on the predicted value of the subject real-estate property.

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TECHNIQUES TO ADD SMART DEVICE INFORMATION TO MACHINE LEARNING FOR INCREASED CONTEXT

Publication No.: US2023021052A1 19/01/2023

Applicant:

CAPITAL ONE SERVICES LLC [US]

US_2020334417_A1

Absstract of: US2023021052A1

Disclosed are an apparatus, a system and a non-transitory computer readable medium that implement processing circuitry that receives non-dialog information from a smart device and determines a data type of data in the received non-dialog information. Based on the determined data type, the processing circuitry transforms the received first data using an input from a machine learning algorithm into transformed data. The transformed data is standardized data that is palatable for machine learning algorithms such as those used implemented as chatbots. The standardized transformed data is useful for training multiple different chatbot systems and enables the typically underutilized non-dialog information to be used to as training input to improve context and conversation flow between a chatbot and a user.

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MULTI-CLASS CLASSIFICATION USING A DUAL MODEL

Publication No.: US2023014551A1 19/01/2023

Applicant:

IBM [US]

Absstract of: US2023014551A1

A method for receiving a full training data set including a plurality of individual training data set, dividing the plurality of individual training sets into N classes, where N is an integer greater than three, dividing the N classes into M full data classes and N-M partial data classes, performing training to obtain a trained fixed size machine learning (ML) classification model and a trained in-class confidence model, outputting a first set of prediction value(s) based on the performance of training, distributing each class of the N classes of individual training data sets to a different node of a distributed machine learning system; and outputting, from the nodes of the distributed machine learning system, a second set of prediction value(s) for each class of the N classes.

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ACCELERATING INFERENCES PERFORMED BY ENSEMBLE MODELS OF BASE LEARNERS

Publication No.: US2023016368A1 19/01/2023

Applicant:

IBM [US]

Absstract of: US2023016368A1

A method is provided for accelerating machine learning inferences. The method uses an ensemble model run on input data. This ensemble model involves several base learners, where each of the base learners has been trained. The method first schedules tasks for execution. As a result of the task scheduling, one of the base learners is executed based on a subset of the input data. The execution of the tasks is then started to obtain respective task outcomes. An exit condition is repeatedly evaluated while executing the tasks by computing a deterministic function of the task outcomes obtained so far. This deterministic function output values indicate whether an inference result of the ensemble model has converged. Accordingly, the execution of the tasks can be interrupted if the exit condition evaluated last is found to be fulfilled. Eventually, an inference result of the ensemble model is estimated based on the task outcomes.

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System, Method, and Computer Program Product for Generating Real-Time Predictions for Vehicle Transportation Requests Using Machine Learning

Nº publicación: US2023017008A1 19/01/2023

Applicant:

VISA INT SERVICE ASS [US]

US_2020074320_PA

Absstract of: US2023017008A1

Described are a system, method, and computer program product for generating real-time predictions for vehicle transportation requests using machine learning. The method includes generating, with a processor and a machine-learning classification model, a transportation categorization for each consumer of a plurality of consumers based on historic transaction data; processing a plurality of new transactions by each consumer, each new transaction associated with a geographic node of activity; in response to processing the new transactions, generating a plurality of vehicle transportation predictions for the consumers based on the transportation categorization for each consumer and a geographic node of activity associated with a new transaction, each vehicle transportation prediction representing a likelihood that the consumer will request vehicle transportation subsequent to conducting the new transaction; and generating a supply map interface comprising a visual identification of a location in which a number of requests for vehicle transportation is predicted to increase based on the vehicle transportation predictions.

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